New TF embeddings (cleaner and faster) (#9418)
* Create new embeddings + add to BERT * Add Albert * Add DistilBert * Add Albert + Electra + Funnel * Add Longformer + Lxmert * Add last models * Apply style * Update the template * Remove unused imports * Rename attribute * Import embeddings in their own model file * Replace word_embeddings per weight * fix naming * Fix Albert * Fix Albert * Fix Longformer * Fix Lxmert Mobilebert and MPNet * Fix copy * Fix template * Update the get weights function * Update src/transformers/modeling_tf_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/electra/modeling_tf_electra.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * address Sylvain's comments Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Julien Plu
@@ -66,6 +66,122 @@ TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
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]
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
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class TF{{cookiecutter.camelcase_modelname}}WordEmbeddings(tf.keras.layers.Layer):
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def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.weight = self.add_weight(
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name="weight",
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shape=[self.vocab_size, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape=input_shape)
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def get_config(self):
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config = {
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"vocab_size": self.vocab_size,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, input_ids):
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flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
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embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
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embeddings = tf.reshape(
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tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
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)
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embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
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return embeddings
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
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class TF{{cookiecutter.camelcase_modelname}}TokenTypeEmbeddings(tf.keras.layers.Layer):
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def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.type_vocab_size = type_vocab_size
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.token_type_embeddings = self.add_weight(
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name="embeddings",
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shape=[self.type_vocab_size, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape=input_shape)
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def get_config(self):
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config = {
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"type_vocab_size": self.type_vocab_size,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, token_type_ids):
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flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
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one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
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embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
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embeddings = tf.reshape(
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tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
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)
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embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
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return embeddings
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
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class TF{{cookiecutter.camelcase_modelname}}PositionEmbeddings(tf.keras.layers.Layer):
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def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.position_embeddings = self.add_weight(
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name="embeddings",
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shape=[self.max_position_embeddings, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape)
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def get_config(self):
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config = {
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"max_position_embeddings": self.max_position_embeddings,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, position_ids):
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input_shape = shape_list(tensor=position_ids)
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position_embeddings = self.position_embeddings[: input_shape[1], :]
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return tf.broadcast_to(input=position_embeddings, shape=input_shape)
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
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class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
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"""Construct the embeddings from word, position and token_type embeddings."""
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@@ -73,121 +189,59 @@ class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.vocab_size = config.vocab_size
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self.hidden_size = config.hidden_size
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self.initializer_range = config.initializer_range
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self.position_embeddings = tf.keras.layers.Embedding(
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config.max_position_embeddings,
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config.hidden_size,
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embeddings_initializer=get_initializer(self.initializer_range),
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self.word_embeddings = TF{{cookiecutter.camelcase_modelname}}WordEmbeddings(
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vocab_size=config.vocab_size,
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hidden_size=config.hidden_size,
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initializer_range=config.initializer_range,
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name="word_embeddings",
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)
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self.position_embeddings = TF{{cookiecutter.camelcase_modelname}}PositionEmbeddings(
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max_position_embeddings=config.max_position_embeddings,
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hidden_size=config.hidden_size,
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initializer_range=config.initializer_range,
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name="position_embeddings",
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)
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self.token_type_embeddings = tf.keras.layers.Embedding(
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config.type_vocab_size,
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config.hidden_size,
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embeddings_initializer=get_initializer(self.initializer_range),
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self.token_type_embeddings = TF{{cookiecutter.camelcase_modelname}}TokenTypeEmbeddings(
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type_vocab_size=config.type_vocab_size,
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hidden_size=config.hidden_size,
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initializer_range=config.initializer_range,
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name="token_type_embeddings",
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)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.embeddings_sum = tf.keras.layers.Add()
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self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
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self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
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def build(self, input_shape):
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"""Build shared word embedding layer """
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with tf.name_scope("word_embeddings"):
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# Create and initialize weights. The random normal initializer was chosen
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# arbitrarily, and works well.
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self.word_embeddings = self.add_weight(
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"weight",
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shape=[self.vocab_size, self.hidden_size],
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initializer=get_initializer(self.initializer_range),
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)
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super().build(input_shape)
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def call(
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self,
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input_ids=None,
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position_ids=None,
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token_type_ids=None,
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inputs_embeds=None,
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mode="embedding",
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training=False,
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):
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def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
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"""
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Get token embeddings of inputs.
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Args:
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inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
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mode: string, a valid value is one of "embedding" and "linear".
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Applies embedding based on inputs tensor.
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Returns:
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outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
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embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
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vocab_size].
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Raises:
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ValueError: if mode is not valid.
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Shared weights logic adapted from
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https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
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final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
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"""
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if mode == "embedding":
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return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
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elif mode == "linear":
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return self._linear(input_ids)
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else:
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raise ValueError("mode {} is not valid.".format(mode))
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def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
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"""Applies embedding based on inputs tensor."""
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assert not (input_ids is None and inputs_embeds is None)
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if input_ids is not None:
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input_shape = shape_list(input_ids)
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else:
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input_shape = shape_list(inputs_embeds)[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
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inputs_embeds = self.word_embeddings(input_ids=input_ids)
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if token_type_ids is None:
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token_type_ids = tf.fill(input_shape, 0)
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input_shape = shape_list(tensor=inputs_embeds)[:-1]
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token_type_ids = tf.fill(dims=input_shape, value=0)
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if inputs_embeds is None:
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inputs_embeds = tf.gather(self.word_embeddings, input_ids)
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if position_ids is None:
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position_embeds = self.position_embeddings(position_ids=inputs_embeds)
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else:
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position_embeds = self.position_embeddings(position_ids=position_ids)
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position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
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token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
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embeddings = inputs_embeds + position_embeddings + token_type_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings, training=training)
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token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
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final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
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final_embeddings = self.LayerNorm(inputs=final_embeddings)
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final_embeddings = self.dropout(inputs=final_embeddings, training=training)
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return embeddings
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def _linear(self, inputs):
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"""
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Computes logits by running inputs through a linear layer.
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Args:
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inputs: A float32 tensor with shape [batch_size, length, hidden_size].
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Returns:
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float32 tensor with shape [batch_size, length, vocab_size].
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"""
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batch_size = shape_list(inputs)[0]
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length = shape_list(inputs)[1]
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x = tf.reshape(inputs, [-1, self.hidden_size])
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logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
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return tf.reshape(logits, [batch_size, length, self.vocab_size])
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return final_embeddings
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention
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class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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@@ -198,8 +252,8 @@ class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer)
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f"of attention heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.query = tf.keras.layers.experimental.EinsumDense(
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equation="abc,cde->abde",
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output_shape=(None, config.num_attention_heads, self.attention_head_size),
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@@ -266,9 +320,9 @@ class TF{{cookiecutter.camelcase_modelname}}SelfOutput(tf.keras.layers.Layer):
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f"of attention heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.all_head_size = config.num_attention_heads * self.attention_head_size
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self.dense = tf.keras.layers.experimental.EinsumDense(
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equation="abcd,cde->abe",
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output_shape=(None, self.all_head_size),
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@@ -450,6 +504,8 @@ class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Lay
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super().__init__(**kwargs)
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self.vocab_size = config.vocab_size
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self.hidden_size = config.hidden_size
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self.transform = TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(config, name="transform")
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# The output weights are the same as the input embeddings, but there is
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@@ -465,7 +521,7 @@ class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Lay
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return self.input_embeddings
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def set_output_embeddings(self, value):
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self.input_embeddings.word_embeddings = value
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self.input_embeddings.weight = value
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self.input_embeddings.vocab_size = shape_list(value)[0]
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def get_bias(self):
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@@ -476,9 +532,12 @@ class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Lay
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self.vocab_size = shape_list(value["bias"])[0]
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def call(self, hidden_states):
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hidden_states = self.transform(hidden_states)
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hidden_states = self.input_embeddings(hidden_states, mode="linear")
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hidden_states = hidden_states + self.bias
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hidden_states = self.transform(hidden_states=hidden_states)
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seq_length = shape_list(tensor=hidden_states)[1]
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hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
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hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
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hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
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hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
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return hidden_states
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@@ -514,11 +573,11 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
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self.config = config
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def get_input_embeddings(self):
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return self.embeddings
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, value):
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self.embeddings.word_embeddings = value
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self.embeddings.vocab_size = shape_list(value)[0]
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self.embeddings.word_embeddings.weight = value
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self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
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def _prune_heads(self, heads_to_prune):
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"""Prunes heads of the model.
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@@ -812,7 +871,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
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)
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self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
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self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
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self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
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def get_lm_head(self):
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return self.mlm.predictions
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@@ -909,7 +968,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca
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logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
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self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
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self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
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self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
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def get_lm_head(self):
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return self.mlm.predictions
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